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| Research on Topic Discovery and Evolution Based on Time Series Clustering |
| Li Hailin and Wu Xianli |
| College of Business Administration, Huaqiao University, Quanzhou 362021 |
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Abstract In view of the uniqueness of the existing methods of topic discovery and evolutionary analysis in literature, this paper proposes a method of topic discovery and evolutionary analysis based on time series clustering. The co-occurrence matrix of high-frequency keywords in document datasets is found by co-word analysis. The co-occurrence matrix is transformed into a similarity matrix by the Ochiia coefficient calculation method, and then the topic of the document is found by using the nearest neighbor propagation clustering algorithm. At the same time, the research heat of each topic during a certain period is analyzed and transformed into time series data reflecting the heat of each topic, and the time series clustering method is used to classify and analyze the evolution trend of each topic. The experimental results show that the proposed method can effectively discover the research topics of journals and better analyze the evolution trends of these topics through data processing and mining of the journal literature related to innovation management in CNKI from 2000 to 2018.
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Received: 30 November 2018
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